TMPRSS2-ERGgene fusion is rare compared toPTENdeletions in stage T1a prostate cancer

Contemporary Update on Clinical and Experimental Prostate Cancer Biomarkers: A Multi-Omics-Focused Approach to Detection and Risk Stratification

Prostate cancer remains a significant health challenge, being the most prevalent non-cutaneous cancer in men worldwide. This review discusses the critical advancements in biomarker discovery using single-omics and multi-omics approaches. Multi-omics, integrating genomic, transcriptomic, proteomic, metabolomic, and epigenomic data, offers a comprehensive understanding of the molecular heterogeneity of prostate cancer, leading to the identification of novel biomarkers and therapeutic targets. This holistic approach not only enhances the specificity and sensitivity of prostate cancer detection but also supports the development of personalized treatment strategies. Key studies highlighted include the identification of novel genes, genetic mutations, peptides, metabolites, and potential biomarkers through multi-omics analyses, which have shown promise in improving prostate cancer management. The integration of multi-omics in clinical practice can potentially revolutionize prostate cancer prognosis and treatment, paving the way for precision medicine. This review underscores the importance of continued research and the application of multi-omics to overcome current challenges in prostate cancer diagnosis and therapy.

Mut2Vec: distributed representation of cancerous mutations

BACKGROUND

Embedding techniques for converting high-dimensional sparse data into low-dimensional distributed representations have been gaining popularity in various fields of research. In deep learning models, embedding is commonly used and proven to be more effective than naive binary representation. However, yet no attempt has been made to embed highly sparse mutation profiles into densely distributed representations. Since binary representation does not capture biological context, its use is limited in many applications such as discovering novel driver mutations. Additionally, training distributed representations of mutations is challenging due to a relatively small amount of available biological data compared with the large amount of text corpus data in text mining fields.

METHODS

We introduce Mut2Vec, a novel computational pipeline that can be used to create a distributed representation of cancerous mutations. Mut2Vec is trained on cancer profiles using Skip-Gram since cancer can be characterized by a series of co-occurring mutations. We also augmented our pipeline with existing information in the biomedical literature and protein-protein interaction networks to compensate for the data insufficiency.

RESULTS

To evaluate our models, we conducted two experiments that involved the following tasks: a) visualizing driver and passenger mutations, b) identifying novel driver mutations using a clustering method. Our visualization showed a clear distinction between passenger mutations and driver mutations. We also found driver mutation candidates and proved that these were true driver mutations based on our literature survey. The pre-trained mutation vectors and the candidate driver mutations are publicly available at http://infos.korea.ac.kr/mut2vec .

CONCLUSIONS

We introduce Mut2Vec that can be utilized to generate distributed representations of mutations and experimentally validate the efficacy of the generated mutation representations. Mut2Vec can be used in various deep learning applications such as cancer classification and drug sensitivity prediction.

Fluorescence in situ hybridization in surgical pathology: principles and applications

Identification of recurrent tumour‐specific chromosomal translocations and novel fusion oncogenes has important diagnostic, therapeutic and prognostic implications. Over the past decade, fluorescence in situ hybridization (FISH) analysis of tumour samples has been one of the most rapidly growing areas in genomic medicine and surgical pathology practice. Unlike traditional cytogenetics, FISH affords a rapid analysis of formalin‐fixed, paraffin‐embedded cells within a routine pathology practice workflow. As more diagnostic and treatment decisions are based on results of FISH, demand for the technology will become more widespread. Common FISH‐detected alterations are chromosome deletions, gains, translocations, amplifications and polysomy. These chromosome alterations may have diagnostic and therapeutic implications for many tumour types. Integrating genomic testing into cancer treatment decisions poses many technical challenges, but rapid progress is being made to overcome these challenges in precision medicine. FISH assessment of chromosomal changes relevant to differential diagnosis and cancer treatment decisions has become an important tool for the surgical pathologist. The aim of this review is to provide a theoretical and practical survey of FISH detected translocations with a focus on strategies for clinical application in surgical pathology practice.